Search Results - (( variable integration model algorithm ) OR ( variable detection using algorithm ))
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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things
Published 2022“…Here, the memory consumption can be reduced by enabling a feature selection algorithm that excludes nonrelevant features and preserves the relevant ones. the algorithm is developed based on the variable length of the PSO. …”
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An Optimized Semantic Segmentation Framework for Human Skin Detection
Published 2024“…The study incorporating optimization strategy in semantic segmentation is underexplored in dermatology. Existing approaches used complex and various heuristic designs of image processing algorithms and deep models customized for skin detection problems. …”
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Predicting Cheaters in PlayerUnknown’s Battlegrounds (PUBG) using Random Forest Algorithm
Published 2023“…The outcomes of this research contribute to the advancement of cheat detection mechanisms in online gaming. The developed prediction model can be integrated into existing systems to improve cheat detection capabilities and promote an enjoyable and fair gaming experience for all PUBG participants…”
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Final Year Project Report / IMRAD -
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A simple model-free butterfly shape-based detection (BSD) method integrated with deep learning CNN for valve stiction detection and quantification
Published 2020“…In most methods for shape-based stiction detection, they rely heavily on the traditional controller output (OP) and process variable (PV) plot (i.e. …”
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Detection of leak size and its location in a water distribution system by using K-NN / Nasereddin Ibrahim Sherksi
Published 2020“…The successfully achieved four set objectives inclusive of (1) a new classification model to detect water leakage, (2) analysis of the effects of leakage size on the variables within a WDS, i.e. flow, pressure, pipe volume, velocity and water demand, (3) locating and specifying the leakage size in the WDS, and (4) evaluate the performance of the designed K-NN algorithm for accurate leak detection. …”
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Modelling of cupping suction system based on system identification method
Published 2022“…The detection of cupped suction system plants using a standard model based on a modified Sine Cosine Algorithm (mSCA) is presented in this research. …”
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Modeling of cupping suction system based on system identification method
Published 2022“…The input and output data were used to create this modeling output variable of the cupping suction system is detected by connecting a differential pressure sensor to the cup, while the input variable is determined by the speed of the pump applied in various locations. …”
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Undergraduates Project Papers -
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Landslide susceptibility mapping using decision-tree based chi-squared automatic interaction detection (CHAID) and logistic regression (LR) integration
Published 2014“…Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. …”
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Anomaly detection of denial-of-service network traffic attacks using autoencoders and isolation forest
Published 2026“…and an accuracy of 6.09%, reflecting the difficulty of modelling complex and highly variable network traffic patterns. …”
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Enhancing safety of micro-mobility and powered mobility devices using YOLOv12-based real-time obstacle detection
Published 2025“…The system integrates a Raspberry Pi 4 with a camera, MPU-6050 accelerometer, and buzzer, utilizing the YOLOv12 object detection model and DeepSORT tracking algorithm. …”
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Proceeding Paper -
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Pure intelligent monitoring system for steam economizer trips
Published 2017“…An integrated plant data preparation framework for 10 trips was studied as framework variables. …”
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An efficient and lightweight detection method for stranded elastic needle defects in complex industrial environments using VEE-YOLO
Published 2025“…The algorithm’s performance was evaluated using a dataset to detect stranded elastic needle defects. …”
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Groundwater quality assessment and optimization of monitored wells using multivariate geostatistical techniques in Amol-Babol Plain, Iran
Published 2015“…The integration of multivariate statistical methods with geostatistical interpolation techniques revealed that salinity and total and faecal coliforms as time independent variables and hardness as a time dependent variable influenced the groundwater quality in the study area. …”
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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…These models are developed by integrating multilayer perceptron neural network and evolutionary optimization techniques. …”
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Non-fungible token based smart manufacturing to scale Industry 4.0 by using augmented reality, deep learning and industrial Internet of Things
Published 2023“…The Feedforward and Convolutional Neural Network were used to classify the target variables in relation with predictive maintenance failure analysis. …”
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Comparing seabed roughness result from QPS fledermaus software, benthic trrain modeler [BTM] and developed model derived FRM slope variability algorithm for hard coral reef detecti...
Published 2018“…In this study, several models has been created which are from QPS Fledermaus model, BTM model and Slope Variability model. Slope variability model is an algorithm that is being used for detecting terrain roughness. …”
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SURE-Autometrics algorithm for model selection in multiple equations
Published 2016“…The algorithm is developed by integrating the SURE model with the Autometrics search strategy; hence, it is named as SURE-Autometrics. …”
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The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration
Published 2018“…The current study investigates an improved version of Least Square Support Vector Machines integrated with a Bat Algorithm (LSSVM-BA) for modeling the dissolved oxygen (DO) concentration in rivers. …”
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Fault diagnostic algorithm for precut fractionation column
Published 2004“…Hazard and Operability Study (HAZOP) is used to support the diagnosis task. The algorithm has been successful in detecting the deviations of each variable by testing the data set. …”
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